Cloud Resource Prediction Platform Optimizing Allocation Efficiency

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Solution Overview

Problem

Cloud computing environments face challenges in managing computing resources efficiently due to difficulties in analyzing vast data points across multiple accounts, leading to poor resource allocation, overuse, and mis-allocation, resulting in wasted resources and increased expenses.

Innovation Solution

A cloud resource prediction platform utilizes a machine learning model trained on historical cloud and customer data to determine usage growth profiles and deviation data, processing requests for new resource usage to generate projected usage data, thereby optimizing resource allocation and reducing inefficiencies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual analysis methods are used to manage cloud computing resources, then operational control is maintained, but resource allocation efficiency deteriorates due to inability to analyze vast data points across multiple accounts

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoiddata analysis complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical analysis methods with an automated machine learning-based prediction platform. The system uses trained machine learning models to automatically analyze vast amounts of historical cloud usage data across multiple accounts, eliminating the need for manual data processing while significantly improving resource allocation efficiency and accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If resources are allocated without predictive analysis, then allocation speed is maintained, but resource utilization efficiency deteriorates due to over-allocation and mis-allocation

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidresource planning time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by using machine learning models to predict future cloud resource usage patterns before actual usage occurs. The system analyzes historical data and generates predictions about future resource demands, allowing cloud providers to proactively allocate resources in advance, thereby improving utilization efficiency while reducing waste from over-allocation.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If cloud computing environments expand to serve multiple organizations, then service coverage is improved, but monitoring and management difficulty increases due to vast data points across multiple accounts

Engineering Contradiction:

Improveservice coverageVSAvoidusage pattern detection difficulty

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent applies universality by creating a multi-functional machine learning prediction platform that can simultaneously analyze and predict resource usage patterns across multiple customer accounts and cloud environments. The system is designed to handle diverse data types and scales, providing universal resource optimization capabilities that work across enterprise clouds, public clouds, and hybrid cloud configurations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If traditional resource allocation methods are used, then implementation simplicity is maintained, but resource management quality deteriorates resulting in wasted resources and increased expenses

Engineering Contradiction:

Improveresource management qualityVSAvoidprediction platform complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements feedback mechanisms where the machine learning models continuously learn from actual resource usage patterns and prediction accuracy. The system compares predicted usage with actual usage, uses this feedback to retrain and improve models, and dynamically adjusts resource allocation recommendations. This closed-loop feedback system significantly improves resource management quality while the system manages the complexity through automation.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11392843B2Utilizing a machine learning model to predict a quantity of cloud resources to allocate to a customer

Publication Date: 2022.07.19 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

A device may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The device may determine a usage growth profile based on the historical cloud data and the historical customer data, and may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual and planned resource usage. The device may train a model, with the usage growth profile and the usage deviation data, to generate a trained model, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data.